Description
Title: A FAST AND EFFECTIVE VIEW SELECTION APPROACH FOR A DATA WAREHOUSE USING TREE MINING AND EVOLUTIONARY COMPUTING
Abstract: The performance of a database is significantly influenced by the choice of the right set of views to materialize. There are numerous view selection techniques that employ various frameworks and techniques to choose an effective set of views for serialization. In this paper, using a tree mining approach and evolutionary optimization, we present a new, efficient, scalable method for view selection under the specified storage constraints. The historical SQL dataset’s (sub)queries’ precise frequency can be found out using the tree mining algorithm. By maximizing the performance benefits from the internal view set, which is derived from the frequent view set provided by the tree mining algorithm, the Query Cost model succeeds in its goal. A query’s performance benefit is determined as a function of its frequency, cost to create and maintain the query, and maintenance cost. The experimental findings demonstrate that the suggested approach is effective in recommending a solution that comes close to being the optimal one.
Keywords: database management systems, data warehousing and data mining, query optimization, graph mining, algorithms for parallel computing, evolutionary
computations, genetic algorithms
Paper Quality: SCOPUS / Web of Science Level Research Paper
Paper type: Analysis Based Research Paper
Subject: Computer Science
Writer Experience: 20+ Years
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